License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.44
URN: urn:nbn:de:0030-drops-189394
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18939/
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Jongwiriyanurak, Natchapon ; Zeng, Zichao ; Wang, Meihui ; Haworth, James ; Tanaksaranond, Garavig ; Boehm, Jan

Framework for Motorcycle Risk Assessment Using Onboard Panoramic Camera (Short Paper)

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LIPIcs-GIScience-2023-44.pdf (3 MB)

Abstract

Traditional safety analysis methods based on historical crash data and simulation models have limitations in capturing real-world driving scenarios. In this experiment, panoramic videos recorded from a motorcyclist’s helmet in Bangkok, Thailand, were narrated using an image-to-text model and then put into a Large Language Model (LLM) to identify potential hazards and assess crash risks. The framework can assess static and moving objects with the potential for early warning and incident analysis. However, the limitations of the existing image-to-text model cause its inability to handle panoramic images effectively.

BibTeX - Entry

@InProceedings{jongwiriyanurak_et_al:LIPIcs.GIScience.2023.44,
  author =	{Jongwiriyanurak, Natchapon and Zeng, Zichao and Wang, Meihui and Haworth, James and Tanaksaranond, Garavig and Boehm, Jan},
  title =	{{Framework for Motorcycle Risk Assessment Using Onboard Panoramic Camera}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{44:1--44:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18939},
  URN =		{urn:nbn:de:0030-drops-189394},
  doi =		{10.4230/LIPIcs.GIScience.2023.44},
  annote =	{Keywords: Traffic incident risk, Large Language Model, Vision-Language Model}
}

Keywords: Traffic incident risk, Large Language Model, Vision-Language Model
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023

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